
# Tensorboard 可视化

import tensorflow as tf
import numpy as np


def add_layer(inputs,in_size,out_size,n_layer,activation_function=None):
    layer_name = 'layer%s' % n_layer
    with tf.name_scope(layer_name):
        with tf.name_scope('weights'):
            Weights = tf.Variable(tf.random_normal([in_size,out_size]), name='W')
            # tf.histogram_summary(layer_name+'/weights',Weights)       #此方法在1.0.0中已无法使用
            tf.summary.histogram(layer_name+'/weights',Weights)
        with tf.name_scope('biases'):
            biases = tf.Variable(tf.zeros([1,out_size]) + 0.1)
        with tf.name_scope('Wx_plus_b'):
            Wx_plus_b = tf.matmul(inputs,Weights) + biases

        if activation_function is None :
            outputs = Wx_plus_b
        else:
            outputs = activation_function(Wx_plus_b,)

        return outputs


x_data = np.linspace(-1,1,300)[:,np.newaxis]        # 线段，输入值
noise = np.random.normal(0,0.05,x_data.shape)       # 噪点
y_data = np.square(x_data) - 0.5 + noise            # 加上噪点的输出值

with tf.name_scope('inputs'):
    xs = tf.placeholder(tf.float32,[None,1],name='x_input')
    ys = tf.placeholder(tf.float32,[None,1],name='y_input')

l1 = add_layer(xs,1,10,n_layer=1,activation_function=tf.nn.relu)          # 隐藏层，一个神经元，隐藏层，10个神经元
prediction = add_layer(l1,10,1,n_layer=2,activation_function=None)             # 输出层，1个神经元

# 输出层与实际输出值的偏差
with tf.name_scope('loss'):
    loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction), reduction_indices=[1]))
with tf.name_scope('train'):
    train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)  # 以0.1的步长减小输出偏差

init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)

for i in range(1000):
    sess.run(train_step,feed_dict={xs:x_data,ys:y_data})
    if i%50 == 0:
        print(sess.run(loss,feed_dict={xs:x_data,ys:y_data}))

# writer = tf.train.SummaryWriter('logs/',sess.graph)
tf.summary.FileWriter('./logs/',sess.graph)



#在命令行中执行 tensorboard --logdir='logs/'


